



'''simple docstring'''






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
						import sqlitea

						import sqlalchemy

class 							SCREAMING_SNAKE_CASE(				A__     ):







	"""simple docstring"""






	def __init__(		self     :	Optional[int]						,     __snake_case     :	Union[str, "sqlalchemy.sql.Selectable"]						,     __snake_case     :	Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]						,     __snake_case     :	Optional[Features] = None						,     __snake_case     :	str = None						,     __snake_case     :	bool = False						,     **__snake_case     :	List[str]						,     )     ->						Dict:
								super().__init__(features=lowercase_						,     cache_dir=lowercase_						,     keep_in_memory=lowercase_						,     **lowercase_						)
								UpperCAmelCase					:			Tuple									=						Sql(
								    cache_dir=lowercase_						,     features=lowercase_						,     sql=lowercase_						,     con=lowercase_						,     **lowercase_						,     )







	def 	A      (		self     :	Optional[Any]						)     ->						Any:
								UpperCAmelCase					:			List[str]									=						None
								UpperCAmelCase					:			Dict									=						None
								UpperCAmelCase					:			Optional[int]									=						None
								UpperCAmelCase					:			str									=						None

								self.builder.download_and_prepare(
								    download_config=lowercase_						,     download_mode=lowercase_						,     verification_mode=lowercase_						,     base_path=lowercase_						,     )

								# Build dataset for splits
								UpperCAmelCase					:			Optional[int]									=						self.builder.as_dataset(
								    split='''train'''						,     verification_mode=lowercase_						,     in_memory=self.keep_in_memory						)
								return dataset

class 							SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self     :	Union[str, Any]						,     __snake_case     :	Dataset						,     __snake_case     :	str						,     __snake_case     :	Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]						,     __snake_case     :	Optional[int] = None						,     __snake_case     :	Optional[int] = None						,     **__snake_case     :	Dict						,     )     ->						Tuple:







								if num_proc is not None and num_proc <= 0:
															raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""						)

								UpperCAmelCase					:			Union[str, Any]									=						dataset
								UpperCAmelCase					:			int									=						name
								UpperCAmelCase					:			Dict									=						con
								UpperCAmelCase					:			Dict									=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								UpperCAmelCase					:			Union[str, Any]									=						num_proc
								UpperCAmelCase					:			Optional[int]									=						to_sql_kwargs







	def 	A      (		self     :	Tuple						)     ->						int:
								UpperCAmelCase					:			Any									=						self.to_sql_kwargs.pop('''sql'''						,     lowercase_						)
								UpperCAmelCase					:			int									=						self.to_sql_kwargs.pop('''con'''						,     lowercase_						)
								UpperCAmelCase					:			str									=						self.to_sql_kwargs.pop('''index'''						,     lowercase_						)

								UpperCAmelCase					:			Union[str, Any]									=						self._write(index=lowercase_						,     **self.to_sql_kwargs						)
								return written







	def 	A      (		self     :	List[str]						,     __snake_case     :	Tuple						)     ->						Optional[Any]:
								UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase					:			int									=						args
								UpperCAmelCase					:			Dict									=						{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
								UpperCAmelCase					:			List[Any]									=						query_table(
								    table=self.dataset.data						,     key=slice(lowercase_						,     offset + self.batch_size						)						,     indices=self.dataset._indices						,     )
								UpperCAmelCase					:			str									=						batch.to_pandas()
								UpperCAmelCase					:			Dict									=						df.to_sql(self.name						,     self.con						,     index=lowercase_						,     **lowercase_						)
								return num_rows or len(lowercase_						)







	def 	A      (		self     :	str						,     __snake_case     :	int						,     **__snake_case     :	str						)     ->						int:
								UpperCAmelCase					:			Dict									=						0

								if self.num_proc is None or self.num_proc == 1:
															for offset in logging.tqdm(
															    range(0						,     len(self.dataset						)						,     self.batch_size						)						,     unit='''ba'''						,     disable=not logging.is_progress_bar_enabled()						,     desc='''Creating SQL from Arrow format'''						,     ):
																						written += self._batch_sql((offset, index, to_sql_kwargs)						)
								else:
															UpperCAmelCase				,						UpperCAmelCase					:			Optional[int]									=						len(self.dataset						), self.batch_size
															with multiprocessing.Pool(self.num_proc						) as pool:
																						for num_rows in logging.tqdm(
																						    pool.imap(
																						        self._batch_sql						,     [(offset, index, to_sql_kwargs) for offset in range(0						,     lowercase_						,     lowercase_						)]						,     )						,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,     unit='''ba'''						,     disable=not logging.is_progress_bar_enabled()						,     desc='''Creating SQL from Arrow format'''						,     ):
																													written += num_rows

								return written






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ForAudioFrameClassification''',
       '''Wav2Vec2ForCTC''',
       '''Wav2Vec2ForMaskedLM''',
       '''Wav2Vec2ForPreTraining''',
       '''Wav2Vec2ForSequenceClassification''',
       '''Wav2Vec2ForXVector''',
       '''Wav2Vec2Model''',
       '''Wav2Vec2PreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWav2Vec2ForCTC''',
       '''TFWav2Vec2Model''',
       '''TFWav2Vec2PreTrainedModel''',
       '''TFWav2Vec2ForSequenceClassification''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''FlaxWav2Vec2ForCTC''',
       '''FlaxWav2Vec2ForPreTraining''',
       '''FlaxWav2Vec2Model''',
       '''FlaxWav2Vec2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




